ENHANCING STUDENT KNOWLEDGE ACQUISITION IN ONLINE LEARNING: A DUAL PROCESSING AND SOCIAL CAPITAL PERSPECTIVE
Bibliographic record
Abstract
Social interaction in online learning can positively impact student learning outcomes, such as knowledge acquisition. As higher education increasingly transitions from traditional face-to-face learning to online platforms, understanding how to enhance student learning outcomes in online learning becomes essential. While social interaction differs in online and offline learning, the extant online learning literature mainly focuses on students’ social interaction frequency or quantity in general, without delving into their use of technologies for social interaction (i.e., social features). To provide a richer process-oriented and social capital perspective, the overarching objective of this dissertation is to understand how the use of social features in online learning can enhance student learning outcomes through emotional and cognitive engagement. More specifically, three research questions are investigated: 1) How does students’ use of social features in online learning affect their emotional and cognitive engagement experience with online learning? 2) How do multiple dimensions of social capital (i.e., structural capital, relational capital, and cognitive capital) moderate the relationship between students’ use of social features in online learning and their emotional/cognitive engagement experience in online learning? 3) How do students’ emotional/cognitive engagement experiences influence their knowledge acquisition in online learning? Drawing on Dual Process and Social Capital theories, this research develops a research model to elucidate how students' use of online social features influences their knowledge acquisition through the dual processes of emotional and cognitive engagement in online learning, and the moderating role of social capital on the impact iv of students’ use of social features in online learning. Data for this study was collected through a survey of participants who had at least one semester of online learning experience in the past three years within a university program. Structural equation modeling was employed for data analysis. The findings indicate that students' use of social features in online learning positively influences both emotional and cognitive engagement, which, in turn, affects knowledge acquisition. Additionally, cognitive capital positively moderates the impact of social feature usage on emotional and cognitive engagement in online learning. Relational capital negatively moderates the impact on cognitive engagement, but not on emotional engagement in online learning. Structural capital positively moderates the impact on cognitive engagement but not on emotional engagement in online learning. This dissertation contributes to the online learning literature by shedding light on how the utilization of social features can interact with students' social capital to influence their engagement, subsequently impacting their knowledge acquisition in online learning. The study advances the existing literature by exploring the intricate interplay between students’ social capital and their use of social features in online learning, elucidating the circumstances under which social resources enhance or impede the impact of such usage. From a practical standpoint, the insights gleaned from this study regarding students' online learning offer valuable guidance for distance educators and policymakers to enhance educational practices within online learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".